Advanced methods for climate and health attribution Summer School

By the end of the summer school, students will be able to:

• Apply advanced epidemiological and attribution methods to assess the health impacts of climate change.
• Design and carry out an independent analysis using real-world or personal datasets, integrating climate and health data.
• Communicate scientific findings effectively, both in written and oral form, through a final presentation to peers and instructors.

The course is structured in two parts:

Phase 1: Learning & Exploration (In-Person)

• Dive into advanced methods in environmental and climate epidemiology
• Learn how to perform probabilistic event attribution
• Engage with experts through lectures, discussions, and hands-on sessions

Phase 2: Independent Project (Remote)

Apply what you’ve learned to a dataset of your choice (or one we provide)
Receive guidance through a mid-term (2 weeks after the course) feedback session with course leads
Present your findings in an online showcase 4 weeks after the course end

Module 1: Probabilistic climate attribution

Course description

Attribution science is a relatively recent branch of climate science, evaluating the extent to which anthropogenic climate change has altered the likelihood and intensity of extreme weather events around the world. This short course provides participants with the knowledge and tools to conduct attribution analysis for extreme weather events, with a particular focus on heatwave events. By the end of the course, students will have the basic skills to conduct their own attribution analyses and calculate factual and counterfactual temperatures to be used in mortality attribution. Topics covered will include:

• Climate attribution
• Visualising weather and climate data
• Defining a meteorological event
• Software tools for probabilistic attribution
• Interpreting attribution results
• The training includes lectures and hands-on computer labs using real data, with time to discuss your own research questions.

Requirements

• To participate, you should:
• Be familiar with conda or similar package managers for installing python and R packages
• Be familiar with spatial/temporal data and common distributions (e.g., normal, Poisson) - helpful but not required
• Bring your own laptop, all exercises will be done using Jupyter Notebook

Software you’ll be introduced to:

• Python for NetCDF data - xarray, xclim, cartopy
• R

Module 2: Bayesian models for climate and environmental health

Course description:

In environmental and climate epidemiology, spatial and spatiotemporal methods are increasingly used with high-resolution environmental and health data, which allows detailed insights into associations between exposures and outcomes. In this context, Bayesian methods provide a natural setting to incorporate uncertainty and prior knowledge, borrow information between neighbouring units, and create hierarchical structures.  However, appropriate usage of these methods requires careful consideration and knowledge of building technical models, which can be intimidating to the uninitiated user. This course provides a practical and approachable introduction to:

• The basics of Bayesian inference
• Hierarchical models
• How to choose priors
• Working with different types of data (spatial, temporal, continuous, categorical)
• Software tools for Bayesian analysis
• Real-world examples and how to apply these methods to your own research
• Bayesian distributed lag non-linear models

The training includes lectures and hands-on computer labs using real data, with time to discuss your own research questions.

Requirements

To participate, you should:

• Know the basics of R and RStudio (e.g., installing packages)
• Be familiar with spatial/temporal data and common distributions (e.g., normal, Poisson), helpful but not required
• Bring your own laptop, all exercises will be done using RStudio Cloud

Software You’ll Be Introduced To

• BUGS / OpenBUGS / WinBUGS
• R-NIMBLE
• R-INLA